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Escher.ipynb
PIBIC
PDH
PFL
Fluxos melhor caso (PDH e MDH)
Fluxos melhor caso (PDH, MDH e AKGDH)
Exportaçao de malato
//IC After Dan/Escher/
Name
ModifiedLast Modified
- e_coli_core.jsonlast mo.
- Escher.ipynb1s ago
- iJO1366.jsonlast mo.
Kernel status: Idle
[1]:
import cobra
from cobra.io import load_json_model
from cobra.flux_analysis import flux_variability_analysis
import pandas as pd
import numpy as np
from cnapy.core import make_scenario_feasible
openjdk version "21.0.8" 2025-07-15 OpenJDK Runtime Environment (Red_Hat-21.0.8.0.9-1) (build 21.0.8+9) OpenJDK 64-Bit Server VM (Red_Hat-21.0.8.0.9-1) (build 21.0.8+9, mixed mode, sharing)
Set parameter Username Set parameter LicenseID to value 2644080 Academic license - for non-commercial use only - expires 2026-03-29
[4]:
R_imp = ['GLCptspp_fw','PDH_fw','PPC_fw','CS_fw','ACONTa_fw','ACONTa_bw',
'ACONTb_fw','ACONTb_bw','ICDHyr_fw','ICDHyr_bw','AKGDH_fw','SUCOAS_fw','SUCOAS_bw',
'FUM_fw','FUM_bw','MDH_fw','MDH_bw']
[5]:
def min_value(model,enzyme):
bound = 0.0
while True:
with model:
model.reactions.get_by_id(enzyme).bounds = (bound,bound)
solution = model.optimize()
if solution.objective_value is not None:
bound -= 0.1
else:
return bound
def max_value(model,enzyme):
bound = 0.0
while True:
with model:
model.reactions.get_by_id(enzyme).bounds = (bound,bound)
solution = model.optimize()
if solution.objective_value is not None:
bound += 0.1
else:
return bound
[7]:
print(f'Max flux FUM_bw: {max_value(model,"FUM_bw")}')
print(f'Min flux FUM_bw: {min_value(model,"FUM_bw")}')
Max flux FUM_bw: 33.3000000000002 Min flux FUM_bw: -0.1
/home/cristian/.conda/envs/cnapy-1.2.4/lib/python3.10/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'. warn(f"Solver status is '{status}'.", UserWarning)
[8]:
print(f'Max flux PDH_fw: {max_value(model,"PDH_fw")}')
print(f'Min flux PDH_fw: {min_value(model,"PDH_fw")}')
Max flux PDH_fw: 11.799999999999974 Min flux PDH_fw: -19.1
[9]:
print(f'Max flux MDH_fw: {max_value(model,"MDH_fw")}')
print(f'Min flux MDH_fw: {min_value(model,"MDH_fw")}')
Max flux MDH_fw: 42.00000000000033 Min flux MDH_fw: -35.10000000000023
[10]:
print(f'Max flux MDH_bw: {max_value(model,"MDH_bw")}')
print(f'Min flux MDH_bw: {min_value(model,"MDH_bw")}')
Max flux MDH_bw: 42.00000000000033 Min flux MDH_bw: -2.700000000000001
[11]:
print(f'Max flux PPC_fw: {max_value(model,"PPC_fw")}')
print(f'Min flux PPC_fw: {min_value(model,"PPC_fw")}')
Max flux PPC_fw: 28.600000000000136 Min flux PPC_fw: -0.1
Max flux GLCptspp_fw: 0.0 Min flux GLCptspp_fw: 0.0
FUM_bw flux: 0.0 PPC_fw flux: 0.41250750342026987 GLCptspp_fw flux: 9.250127204153337 PDH_fw flux: 0.0 MDH_fw flux: 0.0 MDH_bw flux: 0.0
interactive(children=(FloatSlider(value=0.0, description='fum_bw', max=33.3, step=0.01), FloatSlider(value=0.4…
[21]:
| Reaction identifier | MDH_fw |
| Name | Malate dehydrogenase |
| Memory address | 0x7fc7441222c0 |
| Stoichiometry |
0.00236811413346649 enzyme_pool + mal__L_c + nad_c --> h_c + nadh_c + oaa_c 0.00236811413346649 enzyme pool pseudometabolite + L-Malate + Nicotinamide adenine dinucleotide --> H+ + Nicotinamide adenine dinucleotide - reduced + Oxaloacetate |
| GPR | b3236 |
| Lower bound | 0.0 |
| Upper bound | 1000.0 |
[50]:
| Reaction identifier | FUM_bw |
| Name | Fumarase |
| Memory address | 0x7f70707630a0 |
| Stoichiometry |
0.00694546169257057 enzyme_pool + mal__L_c --> fum_c + h2o_c 0.00694546169257057 enzyme pool pseudometabolite + L-Malate --> Fumarate + H2O H2O |
| GPR | b2929 or b1675 or b1612 or b4122 or b1611 |
| Lower bound | 0.0 |
| Upper bound | 1000.0 |
interactive(children=(FloatSlider(value=0.0, description='pdh', max=53.7, step=0.01), FloatSlider(value=0.0, d…
[1]:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import cobra
from cobra.io import load_json_model
[2]:
model = load_json_model('iCH360_h2.json')
Set parameter Username Set parameter LicenseID to value 2644080 Academic license - for non-commercial use only - expires 2026-03-29
[ ]:
[12]:
resultados = {'NADH':[],'H2':[],'Biomassa':[]}
for i in np.arange(0.0, 12, 0.1):
try:
with model:
model.reactions.get_by_id('PDH_fw').bounds = (i,i)
model.reactions.get_by_id('ACALD_fw').bounds = (0,0)
model.reactions.get_by_id('LDH_fw').bounds = (0,0)
solution = model.optimize()
if solution.objective_value is not None:
prod_nadh = sum(model.metabolites.get_by_id('nadh_c').summary(solution).producing_flux['flux'])
prod_h2 = sum(model.metabolites.get_by_id('h2_c').summary(solution).producing_flux['flux'])
resultados['NADH'].append(prod_nadh)
resultados['H2'].append(prod_h2)
resultados['Biomassa'].append(solution.objective_value)
except:
continue
[13]:
resultados
[13]:
{'NADH': [], 'H2': [], 'Biomassa': []}[10]:
| NADH | H2 | Biomassa | |
|---|---|---|---|
| 0 | 11.620166 | 22.951292 | 0.118331 |
| 1 | 11.624185 | 22.959178 | 0.118392 |
| 2 | 11.628203 | 22.967063 | 0.118454 |
| 3 | 11.632221 | 22.974948 | 0.118516 |
| 4 | 11.636239 | 22.982834 | 0.118578 |
| ... | ... | ... | ... |
| 115 | 29.223417 | 23.220535 | 0.036321 |
| 116 | 29.458054 | 23.413378 | 0.035142 |
| 117 | 29.692691 | 23.606222 | 0.033964 |
| 118 | 29.927328 | 23.799065 | 0.032785 |
| 119 | 30.161965 | 23.991909 | 0.031606 |
120 rows × 3 columns
Read LP format model from file /tmp/tmp2dm6hnxe.lp Reading time = 0.00 seconds : 310 rows, 1022 columns, 4870 nonzeros
[15]:
Optimal solution with objective value 0.077
| fluxes | reduced_costs | |
|---|---|---|
| NDPK5_fw | 0.002079 | -2.927346e-18 |
| SHK3Dr_fw | 0.029247 | -1.287490e-18 |
| NDPK6_fw | 0.002014 | -9.714451e-17 |
| NDPK8_fw | 0.002014 | -1.214306e-17 |
| DHORTS_fw | 0.000000 | -9.303853e-03 |
| ... | ... | ... |
| HYDFDN_bw | 0.000000 | -3.580570e-03 |
| PFOR_fw | 4.223553 | -7.589415e-19 |
| EX_h2_e_fw | 16.894211 | 0.000000e+00 |
| H2TPP_fw | 16.894211 | 0.000000e+00 |
| H2tex_fw | 16.894211 | 0.000000e+00 |
511 rows × 2 columns
[28]:
| Biomassa | NADH | |
|---|---|---|
| SA | 0.617 | 28.3100 |
| SAn | 0.103 | 18.5300 |
| M | 26.070 | 0.0102 |
[1]:
import cobra
from cobra.io import load_json_model, save_json_model
[2]:
model = load_json_model('Escherichia_coli_iCH360.json')
Set parameter Username Set parameter LicenseID to value 2644080 Academic license - for non-commercial use only - expires 2026-03-29
[3]:
model.reactions.get_by_id('EX_o2_e').bounds = (0,0)
[4]:
model.add_metabolites([cobra.Metabolite('fdxrd_c',compartment='c'),
cobra.Metabolite('fdxox_c',compartment='c'),
cobra.Metabolite('h2_c',compartment='c'),
cobra.Metabolite('h2_e',compartment='e'),
cobra.Metabolite('h2_p',compartment='p')
])
HYDFDN = cobra.Reaction('HYDFDN')
EX_h2_e = cobra.Reaction('EX_h2_e')
PFOR = cobra.Reaction('PFOR')
H2TPP = cobra.Reaction('H2TPP')
H2tex = cobra.Reaction('H2tex')
model.add_reactions([HYDFDN,
PFOR,
EX_h2_e,
H2TPP,
H2tex])
# Define reaction equations
HYDFDN.reaction = '3.0 h_c + 1.0 nadh_c + 1.0 fdxrd_c <--> 2.0 h2_c + 1.0 nad_c + 1.0 fdxox_c'
PFOR.reaction = '1.0 coa_c + 1.0 pyr_c + 2.0 fdxox_c -> 1.0 accoa_c + 1.0 co2_c + 1.0 h_c + 2.0 fdxrd_c'
H2TPP.reaction = '1 h2_c -> 1 h2_p' #cytosol to periplasm
H2tex.reaction='1 h2_p -> 1 h2_e' #periplasm to external
EX_h2_e.reaction = '1 h2_e ->'
### EC-iCH360
Para modificar o EC-iCH360, o ideal seria colocar o enzyme pool para cada reação. Para isso, a reação da hydrogenase precisa ser separada em duas:
HYDFDN_fw: 3.0 h_c + 1.0 nadh_c + 1.0 fdxrd_c + enzyme_pool_fw-> 2.0 h2_c + 1.0 nad_c + 1.0 fdxox_c
HYDFDN_bw: 2.0 h2_c + 1.0 nad_c + 1.0 fdxox_c + enzyme_pool_bw-> 3.0 h_c + 1.0 nadh_c + 1.0 fdxrd_c
Calculo dos enzyme_pools:
- Massa molecular da HydABC (https://doi.org/10.1021/jacs.2c11683) = (306+348)/2 = 327 kDa
- turnover produção de H2 (https://doi.org/10.1016/S0005-2728(99)00062-6) = (24+40)/2 = 32 s-1
- turnover produção de H2 (https://doi.org/10.1016/S0005-2728(99)00062-6) = (120+190)/2 = 155 s-1
A formula para calular o enzyme pool é: e = M/(turnover*saturation)
Esse saturation irei adotar como 1 para os dois enzymes pool. Também temos que converter os turnovers, uma vez que estão em s-1. Para passar para h-1, multiplicamos por 3600, ficando com:
EC-iCH360¶
Para modificar o EC-iCH360, o ideal seria colocar o enzyme pool para cada reação. Para isso, a reação da hydrogenase precisa ser separada em duas:
HYDFDN_fw: 3.0 h_c + 1.0 nadh_c + 1.0 fdxrd_c + enzyme_pool_fw-> 2.0 h2_c + 1.0 nad_c + 1.0 fdxox_c HYDFDN_bw: 2.0 h2_c + 1.0 nad_c + 1.0 fdxox_c + enzyme_pool_bw-> 3.0 h_c + 1.0 nadh_c + 1.0 fdxrd_c
Calculo dos enzyme_pools:
- Massa molecular da HydABC (https://doi.org/10.1021/jacs.2c11683) = (306+348)/2 = 327 kDa
- turnover produção de H2 (https://doi.org/10.1016/S0005-2728(99)00062-6) = (24+40)/2 = 32 s-1
- turnover produção de H2 (https://doi.org/10.1016/S0005-2728(99)00062-6) = (120+190)/2 = 155 s-1
A formula para calular o enzyme pool é: e = M/(turnover*saturation)
Esse saturation irei adotar como 1 para os dois enzymes pool. Também temos que converter os turnovers, uma vez que estão em s-1. Para passar para h-1, multiplicamos por 3600, ficando com:
- turnover produção H2 = 32*3600 = 115200
- turnover consumo H2 = 155*3600 = 558000
Com isso, temos:
- enzyme_pool_fw = 327/115200 = 0.002838542
- enzyme_pool_bw = 327/558000 = 0.000586022
Observe que a produção de H2 é mais penalizada que o consumo.
Temos que fazer o mesmo para a PFOR:
- turnover (https://www.brenda-enzymes.org/enzyme.php?ecno=1.2.7.1#TURNOVER%20NUMBER%20[1/s]) = 60.5*3600 = 217800 h-1
- Massa molecular (BRENDA): 67 kDa
- enzyme_pool = 67/217800 = 0.000308278
Set parameter Username Set parameter LicenseID to value 2644080 Academic license - for non-commercial use only - expires 2026-03-29
[4]:
np.float64(0.05991273287416082)
[7]:
Optimal solution with objective value 0.120
| fluxes | reduced_costs | |
|---|---|---|
| NDPK5_fw | 0.003234 | 4.662069e-18 |
| SHK3Dr_fw | 0.045490 | 0.000000e+00 |
| NDPK6_fw | 0.003132 | 0.000000e+00 |
| NDPK8_fw | 0.003132 | 0.000000e+00 |
| DHORTS_fw | 0.000000 | -8.574123e-03 |
| ... | ... | ... |
| HYDFDN_bw | 0.000000 | -3.299734e-03 |
| PFOR_fw | 5.781830 | -2.168404e-19 |
| EX_h2_e_fw | 23.127319 | 0.000000e+00 |
| H2TPP_fw | 23.127319 | 0.000000e+00 |
| H2tex_fw | 23.127319 | 0.000000e+00 |
511 rows × 2 columns
[1]:
import pandas as pd
#import matplotlib.pyplot as plt
import cobra
import numpy as np
from cobra.io import load_json_model
from cobra.flux_analysis import flux_variability_analysis
[2]:
# Carregamento do modelo
model = load_json_model('EC_iCH360.json')
Set parameter Username Set parameter LicenseID to value 2644080 Academic license - for non-commercial use only - expires 2026-03-29
[9]:
import re
for i in model.reactions:
i = str(i)
if re.search("(^GLCptspp|^PDH|^PPC|^CS|^ACONT|^ICDHyr|^AKGDH|^SUCOAS|^SUCDi|^FUM|^MDH)", i):
print(i)
CS_fw: accoa_c + 0.00201858684593728 enzyme_pool + h2o_c + oaa_c --> cit_c + coa_c + h_c ICDHyr_fw: 0.00314748314882975 enzyme_pool + icit_c + nadp_c --> akg_c + co2_c + nadph_c PPCK_fw: atp_c + 0.0010183357374514 enzyme_pool + oaa_c --> adp_c + co2_c + pep_c MDH_fw: 0.00236811413346649 enzyme_pool + mal__L_c + nad_c --> h_c + nadh_c + oaa_c FUM_fw: 0.000286427523176266 enzyme_pool + fum_c + h2o_c --> mal__L_c PPC_fw: co2_c + 0.00140809547215078 enzyme_pool + h2o_c + pep_c --> h_c + oaa_c + pi_c AKGDH_fw: akg_c + coa_c + 0.00293247443496605 enzyme_pool + nad_c --> co2_c + nadh_c + succoa_c FUMt2_2pp_fw: 5.51101888723051e-05 enzyme_pool + fum_p + 2.0 h_p --> fum_c + 2.0 h_c FUMtex_fw: 0.000301543351233866 enzyme_pool + fum_e --> fum_p GLCptspp_fw: 0.000327143489166588 enzyme_pool + glc__D_p + pep_c --> g6p_c + pyr_c PDH_fw: coa_c + 0.00128810216333717 enzyme_pool + nad_c + pyr_c --> accoa_c + co2_c + nadh_c SUCOAS_fw: atp_c + coa_c + 0.0043694102601719 enzyme_pool + succ_c --> adp_c + pi_c + succoa_c ACONTb_fw: acon_C_c + 0.00151958365772561 enzyme_pool + h2o_c --> icit_c SUCDi_fw: 0.000703990974907043 enzyme_pool + q8_c + succ_c --> fum_c + q8h2_c ACONTa_fw: cit_c + 0.000956384163761154 enzyme_pool --> acon_C_c + h2o_c ICDHyr_bw: akg_c + co2_c + 0.00629504980277568 enzyme_pool + nadph_c --> icit_c + nadp_c MDH_bw: 0.0033676597842785 enzyme_pool + h_c + nadh_c + oaa_c --> mal__L_c + nad_c FUM_bw: 0.00694546169257057 enzyme_pool + mal__L_c --> fum_c + h2o_c FUMtex_bw: 0.000320175727991231 enzyme_pool + fum_p --> fum_e SUCOAS_bw: adp_c + 0.00111566402738508 enzyme_pool + pi_c + succoa_c --> atp_c + coa_c + succ_c ACONTb_bw: 0.00341252350407859 enzyme_pool + icit_c --> acon_C_c + h2o_c ACONTa_bw: acon_C_c + 0.00600259036351675 enzyme_pool + h2o_c --> cit_c
[3]:
R_imp = ['GLCptspp_fw','PDH_fw','PPC_fw','CS_fw','ACONTa_fw','ACONTa_bw',
'ACONTb_fw','ACONTb_bw','ICDHyr_fw','ICDHyr_bw','AKGDH_fw','SUCOAS_fw','SUCOAS_bw',
'SUCDi_fw','FUM_fw','FUM_bw','MDH_fw','MDH_bw']
[4]:
loop_reactions = [model.reactions.get_by_id(i) for i in R_imp]
df_fva_aero = flux_variability_analysis(model, reaction_list=loop_reactions,
loopless=True,fraction_of_optimum=0.9)
[5]:
df_fva_aero
[5]:
| minimum | maximum | |
|---|---|---|
| GLCptspp_fw | 6.514073 | 13.406697 |
| PDH_fw | 0.938670 | 12.450549 |
| PPC_fw | 0.000000 | 9.549213 |
| CS_fw | 0.594377 | 5.833653 |
| ACONTa_fw | 0.594377 | 6.834471 |
| ACONTa_bw | 0.000000 | 3.889498 |
| ACONTb_fw | 0.594377 | 7.760801 |
| ACONTb_bw | 0.000000 | 5.487901 |
| ICDHyr_fw | 0.594377 | 6.242847 |
| ICDHyr_bw | 0.000000 | 2.866489 |
| AKGDH_fw | 0.000000 | 4.843314 |
| SUCOAS_fw | 0.000000 | 5.089382 |
| SUCOAS_bw | 0.000000 | 6.553354 |
| SUCDi_fw | 0.000000 | 5.967491 |
| FUM_fw | 0.000000 | 6.733280 |
| FUM_bw | 0.000000 | 3.742717 |
| MDH_fw | 0.000000 | 7.732268 |
| MDH_bw | 0.000000 | 4.718965 |
# Anaero
Anaero¶
Read LP format model from file /tmp/tmp34q97alk.lp Reading time = 0.00 seconds : 305 rows, 1010 columns, 4816 nonzeros
[9]:
| minimum | maximum | |
|---|---|---|
| GLCptspp_fw | 8.480702 | 11.157505 |
| PDH_fw | 0.000000 | 1.563670 |
| PPC_fw | 0.305451 | 3.604094 |
| CS_fw | 0.099471 | 0.490815 |
| ACONTa_fw | 0.099471 | 3.557768 |
| ACONTa_bw | 0.000000 | 3.458297 |
| ACONTb_fw | 0.099471 | 4.978967 |
| ACONTb_bw | 0.000000 | 4.879497 |
| ICDHyr_fw | 0.099471 | 2.648173 |
| ICDHyr_bw | 0.000000 | 2.548702 |
| AKGDH_fw | 0.000000 | 0.276540 |
| SUCOAS_fw | 0.000000 | 4.436308 |
| SUCOAS_bw | 0.000000 | 4.387580 |
| SUCDi_fw | 0.000000 | 0.000000 |
| FUM_fw | 0.000000 | 3.334754 |
| FUM_bw | 0.000000 | 3.328234 |
| MDH_fw | 0.000000 | 4.195807 |
| MDH_bw | 0.000000 | 4.195807 |
[10]:
| minimum | maximum | |
|---|---|---|
| SUCDi_fw | 0.0 | 0.0 |
[11]:
| minimum | maximum | |
|---|---|---|
| GLCptspp_fw | 8.480702 | 11.157505 |
| PDH_fw | 0.000000 | 1.563670 |
| PPC_fw | 0.305451 | 3.604094 |
| CS_fw | 0.099471 | 0.490815 |
| ACONTa_fw | 0.099471 | 3.557768 |
| ACONTa_bw | 0.000000 | 3.458297 |
| ACONTb_fw | 0.099471 | 4.978967 |
| ACONTb_bw | 0.000000 | 4.879497 |
| ICDHyr_fw | 0.099471 | 2.648173 |
| ICDHyr_bw | 0.000000 | 2.548702 |
| AKGDH_fw | 0.000000 | 0.276540 |
| SUCOAS_fw | 0.000000 | 4.436308 |
| SUCOAS_bw | 0.000000 | 4.387580 |
| FUM_fw | 0.000000 | 3.334754 |
| FUM_bw | 0.000000 | 3.328234 |
| MDH_fw | 0.000000 | 4.195807 |
| MDH_bw | 0.000000 | 4.195807 |
[2]:
| minimum | maximum | |
|---|---|---|
| GLCptspp | 1.260000 | 10.000000 |
| PDH | 0.000000 | 8.740000 |
| PPC | 0.544038 | 4.515155 |
| CS | 0.233694 | 2.418694 |
| ACONTa | 0.233694 | 1.028240 |
| ACONTb | 0.233694 | 1.028240 |
| ICDHyr | 0.233694 | 1.028240 |
| AKGDH | 0.000000 | 0.728333 |
| SUCOAS | 0.000000 | 1.206579 |
| FUM | -2.758714 | 4.524619 |
| MDH | -3.644630 | 4.525163 |
[12]:
{'GLCptspp_fw': [8.480702017782255,
8.778124559286447,
9.075547100790638,
9.37296964229483,
9.670392183799024,
9.967814725303215,
10.265237266807407,
10.562659808311599,
10.86008234981579,
11.157504891319983],
'PDH_fw': [0.0,
0.17374116081433766,
0.3474823216286753,
0.521223482443013,
0.6949646432573506,
0.8687058040716883,
1.042446964886026,
1.2161881257003637,
1.3899292865147013,
1.563670447329039],
'PPC_fw': [0.30545066403546095,
0.6719665350904158,
1.0384824061453708,
1.4049982772003253,
1.7715141482552803,
2.1380300193102353,
2.50454589036519,
2.871061761420145,
3.2375776324750998,
3.6040935035300548],
'CS_fw': [0.09947094082781305,
0.14295357129864042,
0.1864362017694678,
0.2299188322402952,
0.27340146271112253,
0.31688409318194993,
0.36036672365277733,
0.4038493541236047,
0.4473319845944321,
0.4908146150652595],
'ACONTa_fw': [0.09947094082781305,
0.4837261481877941,
0.8679813555477751,
1.2522365629077563,
1.636491770267737,
2.0207469776277183,
2.4050021849876995,
2.78925739234768,
3.1735125997076614,
3.5577678070676426],
'ACONTa_bw': [0.0,
0.38425520735998103,
0.7685104147199621,
1.1527656220799432,
1.5370208294399241,
1.921276036799905,
2.3055312441598863,
2.689786451519867,
3.0740416588798483,
3.4582968662398295],
'ACONTb_fw': [0.09947094082781305,
0.6416372223515296,
1.1838035038752461,
1.7259697853989628,
2.2681360669226796,
2.8103023484463963,
3.3524686299701125,
3.8946349114938292,
4.436801193017546,
4.978967474541262],
'ACONTb_bw': [0.0,
0.5421662815237166,
1.0843325630474332,
1.6264988445711497,
2.1686651260948664,
2.710831407618583,
3.2529976891422994,
3.795163970666016,
4.337330252189733,
4.879496533713449],
'ICDHyr_fw': [0.09947094082781305,
0.3826600187687306,
0.6658490967096482,
0.9490381746505657,
1.2322272525914832,
1.515416330532401,
1.7986054084733185,
2.081794486414236,
2.3649835643551538,
2.648172642296071],
'ICDHyr_bw': [0.0,
0.2831890779409176,
0.5663781558818352,
0.8495672338227527,
1.1327563117636703,
1.415945389704588,
1.6991344676455054,
1.982323545586423,
2.2655126235273406,
2.548701701468258],
'AKGDH_fw': [0.0,
0.030726721805839893,
0.06145344361167979,
0.09218016541751968,
0.12290688722335957,
0.15363360902919948,
0.18436033083503936,
0.21508705264087924,
0.24581377444671915,
0.27654049625255905],
'SUCOAS_fw': [0.0,
0.4929231074666134,
0.9858462149332268,
1.4787693223998402,
1.9716924298664535,
2.464615537333067,
2.9575386447996803,
3.4504617522662935,
3.943384859732907,
4.436307967199521],
'SUCOAS_bw': [0.0,
0.4875088394077107,
0.9750176788154215,
1.4625265182231322,
1.950035357630843,
2.4375441970385534,
2.9250530364462644,
3.4125618758539753,
3.900070715261686,
4.387579554669396],
'FUM_fw': [0.0,
0.3705282643807965,
0.741056528761593,
1.1115847931423894,
1.482113057523186,
1.8526413219039826,
2.223169586284779,
2.5936978506655755,
2.964226115046372,
3.3347543794271686],
'FUM_bw': [0.0,
0.3698037338214186,
0.7396074676428372,
1.1094112014642559,
1.4792149352856745,
1.8490186691070931,
2.2188224029285117,
2.58862613674993,
2.958429870571349,
3.3282336043927674],
'MDH_fw': [0.0,
0.4662007670349644,
0.9324015340699288,
1.3986023011048931,
1.8648030681398575,
2.3310038351748217,
2.7972046022097863,
3.263405369244751,
3.729606136279715,
4.195806903314679],
'MDH_bw': [0.0,
0.4662007670349644,
0.9324015340699288,
1.3986023011048931,
1.8648030681398575,
2.3310038351748217,
2.7972046022097863,
3.263405369244751,
3.729606136279715,
4.195806903314679]}[13]:
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| GLCptspp_fw | 8.480702 | 8.778125 | 9.075547 | 9.372970 | 9.670392 | 9.967815 | 10.265237 | 10.562660 | 10.860082 | 11.157505 |
| PDH_fw | 0.000000 | 0.173741 | 0.347482 | 0.521223 | 0.694965 | 0.868706 | 1.042447 | 1.216188 | 1.389929 | 1.563670 |
| PPC_fw | 0.305451 | 0.671967 | 1.038482 | 1.404998 | 1.771514 | 2.138030 | 2.504546 | 2.871062 | 3.237578 | 3.604094 |
| CS_fw | 0.099471 | 0.142954 | 0.186436 | 0.229919 | 0.273401 | 0.316884 | 0.360367 | 0.403849 | 0.447332 | 0.490815 |
| ACONTa_fw | 0.099471 | 0.483726 | 0.867981 | 1.252237 | 1.636492 | 2.020747 | 2.405002 | 2.789257 | 3.173513 | 3.557768 |
| ACONTa_bw | 0.000000 | 0.384255 | 0.768510 | 1.152766 | 1.537021 | 1.921276 | 2.305531 | 2.689786 | 3.074042 | 3.458297 |
| ACONTb_fw | 0.099471 | 0.641637 | 1.183804 | 1.725970 | 2.268136 | 2.810302 | 3.352469 | 3.894635 | 4.436801 | 4.978967 |
| ACONTb_bw | 0.000000 | 0.542166 | 1.084333 | 1.626499 | 2.168665 | 2.710831 | 3.252998 | 3.795164 | 4.337330 | 4.879497 |
| ICDHyr_fw | 0.099471 | 0.382660 | 0.665849 | 0.949038 | 1.232227 | 1.515416 | 1.798605 | 2.081794 | 2.364984 | 2.648173 |
| ICDHyr_bw | 0.000000 | 0.283189 | 0.566378 | 0.849567 | 1.132756 | 1.415945 | 1.699134 | 1.982324 | 2.265513 | 2.548702 |
| AKGDH_fw | 0.000000 | 0.030727 | 0.061453 | 0.092180 | 0.122907 | 0.153634 | 0.184360 | 0.215087 | 0.245814 | 0.276540 |
| SUCOAS_fw | 0.000000 | 0.492923 | 0.985846 | 1.478769 | 1.971692 | 2.464616 | 2.957539 | 3.450462 | 3.943385 | 4.436308 |
| SUCOAS_bw | 0.000000 | 0.487509 | 0.975018 | 1.462527 | 1.950035 | 2.437544 | 2.925053 | 3.412562 | 3.900071 | 4.387580 |
| FUM_fw | 0.000000 | 0.370528 | 0.741057 | 1.111585 | 1.482113 | 1.852641 | 2.223170 | 2.593698 | 2.964226 | 3.334754 |
| FUM_bw | 0.000000 | 0.369804 | 0.739607 | 1.109411 | 1.479215 | 1.849019 | 2.218822 | 2.588626 | 2.958430 | 3.328234 |
| MDH_fw | 0.000000 | 0.466201 | 0.932402 | 1.398602 | 1.864803 | 2.331004 | 2.797205 | 3.263405 | 3.729606 | 4.195807 |
| MDH_bw | 0.000000 | 0.466201 | 0.932402 | 1.398602 | 1.864803 | 2.331004 | 2.797205 | 3.263405 | 3.729606 | 4.195807 |
[20]:
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10.562660 | 1.216188 | 1.404998 | 0.099471 | 3.557768 | 3.074042 | 3.352469 | 0.542166 | 1.232227 | 1.982324 | 0.092180 | 1.478769 | 2.925053 | 0.741057 | 3.328234 | 3.263405 | 0.000000 |
| 1 | 9.670392 | 0.173741 | 2.871062 | 0.360367 | 0.867981 | 1.537021 | 1.725970 | 0.000000 | 0.949038 | 0.000000 | 0.092180 | 0.492923 | 2.925053 | 1.852641 | 1.849019 | 2.331004 | 4.195807 |
| 2 | 9.967815 | 1.389929 | 3.604094 | 0.316884 | 3.557768 | 2.305531 | 1.725970 | 0.542166 | 2.081794 | 0.566378 | 0.153634 | 2.464616 | 2.437544 | 1.852641 | 1.109411 | 0.932402 | 0.932402 |
| 3 | 11.157505 | 0.173741 | 2.504546 | 0.360367 | 3.173513 | 1.537021 | 4.978967 | 2.168665 | 1.798605 | 2.548702 | 0.030727 | 1.971692 | 0.000000 | 0.370528 | 3.328234 | 2.331004 | 0.932402 |
| 4 | 10.860082 | 0.694965 | 3.604094 | 0.447332 | 0.483726 | 0.768510 | 2.810302 | 0.542166 | 0.665849 | 1.699134 | 0.030727 | 0.985846 | 0.000000 | 2.593698 | 2.218822 | 2.331004 | 4.195807 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 999995 | 10.860082 | 0.000000 | 0.671967 | 0.490815 | 3.173513 | 0.000000 | 3.352469 | 4.337330 | 0.099471 | 0.849567 | 0.122907 | 1.478769 | 0.487509 | 0.370528 | 0.369804 | 1.864803 | 4.195807 |
| 999996 | 8.480702 | 0.694965 | 2.138030 | 0.229919 | 3.557768 | 1.921276 | 1.725970 | 4.337330 | 1.798605 | 2.548702 | 0.215087 | 2.957539 | 2.437544 | 1.852641 | 3.328234 | 1.398602 | 0.466201 |
| 999997 | 8.778125 | 0.521223 | 3.237578 | 0.316884 | 3.557768 | 1.152766 | 1.725970 | 4.337330 | 1.232227 | 0.566378 | 0.000000 | 1.478769 | 2.437544 | 3.334754 | 1.479215 | 2.331004 | 0.000000 |
| 999998 | 9.967815 | 0.521223 | 1.771514 | 0.403849 | 1.636492 | 2.305531 | 3.352469 | 1.084333 | 1.232227 | 2.548702 | 0.122907 | 2.957539 | 1.462527 | 2.593698 | 1.479215 | 0.932402 | 3.263405 |
| 999999 | 10.562660 | 1.216188 | 0.671967 | 0.273401 | 2.405002 | 1.921276 | 3.352469 | 3.795164 | 2.364984 | 0.283189 | 0.184360 | 3.450462 | 2.925053 | 0.000000 | 2.218822 | 3.729606 | 1.864803 |
1000000 rows × 17 columns
[23]:
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10.562660 | 1.216188 | 1.404998 | 0.099471 | 3.557768 | 3.074042 | 3.352469 | 0.542166 | 1.232227 | 1.982324 | 0.092180 | 1.478769 | 2.925053 | 0.741057 | 3.328234 | 3.263405 | 0.000000 |
| 1 | 9.670392 | 0.173741 | 2.871062 | 0.360367 | 0.867981 | 1.537021 | 1.725970 | 0.000000 | 0.949038 | 0.000000 | 0.092180 | 0.492923 | 2.925053 | 1.852641 | 1.849019 | 2.331004 | 4.195807 |
| 2 | 9.967815 | 1.389929 | 3.604094 | 0.316884 | 3.557768 | 2.305531 | 1.725970 | 0.542166 | 2.081794 | 0.566378 | 0.153634 | 2.464616 | 2.437544 | 1.852641 | 1.109411 | 0.932402 | 0.932402 |
| 3 | 11.157505 | 0.173741 | 2.504546 | 0.360367 | 3.173513 | 1.537021 | 4.978967 | 2.168665 | 1.798605 | 2.548702 | 0.030727 | 1.971692 | 0.000000 | 0.370528 | 3.328234 | 2.331004 | 0.932402 |
| 4 | 10.860082 | 0.694965 | 3.604094 | 0.447332 | 0.483726 | 0.768510 | 2.810302 | 0.542166 | 0.665849 | 1.699134 | 0.030727 | 0.985846 | 0.000000 | 2.593698 | 2.218822 | 2.331004 | 4.195807 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 999995 | 10.860082 | 0.000000 | 0.671967 | 0.490815 | 3.173513 | 0.000000 | 3.352469 | 4.337330 | 0.099471 | 0.849567 | 0.122907 | 1.478769 | 0.487509 | 0.370528 | 0.369804 | 1.864803 | 4.195807 |
| 999996 | 8.480702 | 0.694965 | 2.138030 | 0.229919 | 3.557768 | 1.921276 | 1.725970 | 4.337330 | 1.798605 | 2.548702 | 0.215087 | 2.957539 | 2.437544 | 1.852641 | 3.328234 | 1.398602 | 0.466201 |
| 999997 | 8.778125 | 0.521223 | 3.237578 | 0.316884 | 3.557768 | 1.152766 | 1.725970 | 4.337330 | 1.232227 | 0.566378 | 0.000000 | 1.478769 | 2.437544 | 3.334754 | 1.479215 | 2.331004 | 0.000000 |
| 999998 | 9.967815 | 0.521223 | 1.771514 | 0.403849 | 1.636492 | 2.305531 | 3.352469 | 1.084333 | 1.232227 | 2.548702 | 0.122907 | 2.957539 | 1.462527 | 2.593698 | 1.479215 | 0.932402 | 3.263405 |
| 999999 | 10.562660 | 1.216188 | 0.671967 | 0.273401 | 2.405002 | 1.921276 | 3.352469 | 3.795164 | 2.364984 | 0.283189 | 0.184360 | 3.450462 | 2.925053 | 0.000000 | 2.218822 | 3.729606 | 1.864803 |
1000000 rows × 17 columns
[1]:
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import cobra
import numpy as np
from cobra.io import load_model
from cnapy.core import make_scenario_feasible
openjdk version "23.0.2-internal" 2025-01-21 OpenJDK Runtime Environment (build 23.0.2-internal-adhoc.conda.src) OpenJDK 64-Bit Server VM (build 23.0.2-internal-adhoc.conda.src, mixed mode, sharing)
[2]:
# Carregar o modelo iJO1366
model_anaero = load_model('iJO1366')
model_anaero.reactions.get_by_id('EX_o2_e').bounds = (0,0)
# As reaçoes que serao tratadas
R_imp = ['GLCptspp','PDH','PPC','CS','ACONTa','ACONTb','ICDHyr','AKGDH','SUCOAS','FUM','MDH']
# Carregar todas as 1000000 de combinaçoes geradas na pt_1
# Esse .to_dict('list') vai fazer um dicionario com as chaves sendo as enzimas e os valores sendo uma lista com os 1000000 de valores das mesmas
df_combinaçoes = pd.read_csv('combinaçoes.csv')
combinaçoes = df_combinaçoes.to_dict('list')
combinations = []
for i in range(100):
x = []
for j in range(11):
x.append(combinaçoes[R_imp[j]][i])
combinations.append(x)
# Inicializa os CSVs com os headers (apenas 1 vez)
pd.DataFrame(columns = ['Combinaçao','Produçao de NADH','Crescimento']).to_csv('df_final_combinations.csv', index=False)
pd.DataFrame(columns = R_imp + ['Combinaçao Final']).to_csv('df_final_changes.csv', index=False)
# Loop principal
for i in range(len(combinations)):
# Variavel para ver se precisa entrar ou nao no algoritmo de resolver a inviabilidade
entrou = False
try:
# Vai criar os dicionarios para armazenarem os valores calculados
dicio_analisado = {'Combinaçao':[],'Produçao de NADH':[],'Crescimento':[]}
dicio_mudanças = {k: [] for k in R_imp}
# Esse entra so quando precisa de alteraçao nos valores das enzimas para dar feasible
Set parameter Username Set parameter LicenseID to value 2644080 Academic license - for non-commercial use only - expires 2026-03-29 <Solution 0.618 at 0x7fdd0ebe8d30> <Solution 1.505 at 0x7fdd0ebd6080> <Solution 0.883 at 0x7fdd0ea8c1c0> <Solution 0.421 at 0x7fdd0eb7dc30> <Solution 1.554 at 0x7fdd0ebff4f0> <Solution 5.628 at 0x7fdd0ea51000> <Solution 0.348 at 0x7fdd0ea76aa0> <Solution 0.088 at 0x7fdd0ea58580> <Solution 0.486 at 0x7fdd0eab20e0> <Solution 0.750 at 0x7fdd0e92b910> <Solution 0.618 at 0x7fdd0e981570> <Solution 5.401 at 0x7fdd0e963040> <Solution 3.273 at 0x7fdd0e8f8a90> <Solution 1.279 at 0x7fdd0e7824d0> <Solution 0.088 at 0x7fdd0e94dfc0> <Solution 1.707 at 0x7fdd0ea52ef0> <Solution 2.455 at 0x7fdd0eb0c160> <Solution 0.375 at 0x7fdd0ebff1f0> <Solution 2.533 at 0x7fdd0eb4cfd0> <Solution 0.348 at 0x7fdd0eb6e890> <Solution 7.227 at 0x7fdd0eb78e20> <Solution 0.686 at 0x7fdd0eb67e80> <Solution 1.474 at 0x7fdd0ecfa200> <Solution 0.177 at 0x7fdd0ec0c700> <Solution 0.530 at 0x7fdd0ebebcd0> <Solution 0.530 at 0x7fdd0eb73dc0> <Solution 1.640 at 0x7fdd0e8f9de0> <Solution 0.199 at 0x7fdd0eb870a0> <Solution 1.504 at 0x7fdd0eab30a0> <Solution 1.687 at 0x7fdd0e8cc730> <Solution 0.252 at 0x7fdd0e7c3a60> <Solution 0.706 at 0x7fdd0ebacb80> <Solution 0.265 at 0x7fdd0ea09de0> <Solution 0.857 at 0x7fdd0e99f0d0> <Solution 0.177 at 0x7fdd0e8703a0> <Solution 0.061 at 0x7fdd0e821510> <Solution 0.890 at 0x7fdd0e88e6e0> <Solution 0.925 at 0x7fdd0e83f850> <Solution 0.706 at 0x7fdd0e6c08b0> <Solution 1.909 at 0x7fdd0e649990> <Solution 2.870 at 0x7fdd0e6ae7d0> <Solution 5.245 at 0x7fdd0e6b3af0> <Solution 5.161 at 0x7fdd0e644d00> <Solution 0.171 at 0x7fdd0e6d4970> <Solution 0.767 at 0x7fdd0e6c4a00> <Solution 0.598 at 0x7fdd0e6d8880> <Solution 2.313 at 0x7fdd0e5945b0> <Solution 4.053 at 0x7fdd0e4fc310> <Solution 3.282 at 0x7fdd0e5f80d0> <Solution 0.905 at 0x7fdd0e5fbe20> <Solution 0.441 at 0x7fdd0e5d3b50> <Solution 4.667 at 0x7fdd0e4cb910> <Solution 4.330 at 0x7fdd0e46f7c0> <Solution 0.620 at 0x7fdd0e869300> <Solution 1.127 at 0x7fdd0ea8e500> <Solution 0.177 at 0x7fdd0eb71d20> <Solution 1.997 at 0x7fdd0eb0d510> <Solution 3.798 at 0x7fdd0ebeaa70> <Solution 2.255 at 0x7fdd0ea5b160> <Solution 0.441 at 0x7fdd0e99e8f0> <Solution 0.618 at 0x7fdd0e4c84f0> <Solution 0.355 at 0x7fdd0e7009d0> <Solution 3.861 at 0x7fdd0e500f70> <Solution 4.160 at 0x7fdd0e661420> <Solution 0.441 at 0x7fdd0e1c9990> <Solution 2.716 at 0x7fdd0e199de0> <Solution 0.845 at 0x7fdd0e6c5f60> <Solution 0.333 at 0x7fdd0e9516f0> <Solution 0.902 at 0x7fdd0e6b2e30> <Solution 0.267 at 0x7fdd0eab9480> <Solution 1.150 at 0x7fdd0e7c2500> <Solution 2.421 at 0x7fdd0ea81c30> <Solution 0.053 at 0x7fdd0eb4e080> <Solution 0.776 at 0x7fdd0ebaea40> <Solution 2.168 at 0x7fdd0e9c77c0> <Solution 0.745 at 0x7fdd0eb7a140> <Solution 1.190 at 0x7fdd0e86a3e0> <Solution 1.400 at 0x7fdd0e5d15a0> <Solution 3.397 at 0x7fdd0e13d720> <Solution 1.168 at 0x7fdd0e369fc0> <Solution 0.949 at 0x7fdd0df8a1a0> <Solution 3.293 at 0x7fdd0e46fb80> <Solution 1.504 at 0x7fdd0e8d7250> <Solution 0.795 at 0x7fdd0e6c2f20> <Solution 1.546 at 0x7fdd0ea8ee00> <Solution 0.353 at 0x7fdd0ebfc430> <Solution 1.780 at 0x7fdd0eb7f220> <Solution 1.721 at 0x7fdd0eb0f190> <Solution 3.256 at 0x7fdd0e8f9600> <Solution 5.368 at 0x7fdd0ebd7e20> <Solution 0.618 at 0x7fdd0ea67970> <Solution 0.752 at 0x7fdd0ea76a70> <Solution 1.980 at 0x7fdd0e83eb00> <Solution 2.699 at 0x7fdd0e8cee00> <Solution 1.977 at 0x7fdd0e72e590> <Solution 0.593 at 0x7fdd0e3978e0> <Solution 0.618 at 0x7fdd0e241ff0> <Solution 2.499 at 0x7fdd0e4c8430> <Solution 4.468 at 0x7fdd0e052020> <Solution 0.318 at 0x7fdd0eb9e380>
[12]:
import multiprocessing
[1]:
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler, StandardScaler
[2]:
df = pd.read_csv('Final_Data.csv')
[49]:
df
[49]:
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10.562660 | 1.216188 | 1.404998 | 0.483726 | 3.557768 | 3.074042 | 1.025892 | 0.542166 | 1.232227 | 1.232227 | 0.00000 | 1.478769 | 1.478769 | 0.741057 | 1.224783 | 0.000000 | 0.000000 | 16.679557 | 0.000000 |
| 1 | 9.670392 | 0.173741 | 2.871062 | 0.360367 | 0.867981 | 0.507615 | 0.360367 | 0.000000 | 0.360367 | 0.000000 | 0.09218 | 0.492923 | 0.582421 | 1.852641 | 1.849019 | 2.331004 | 4.195807 | 17.698335 | 0.005113 |
| 2 | 9.967815 | 1.389929 | 3.604094 | 1.183804 | 3.489335 | 2.305531 | 1.725970 | 0.542166 | 1.750182 | 0.566378 | 0.00000 | 2.454197 | 2.437544 | 1.131901 | 1.109411 | 0.932402 | 0.932402 | 19.835517 | 0.031742 |
| 3 | 11.157505 | 0.173741 | 2.504546 | 1.636492 | 3.173513 | 1.537021 | 3.805157 | 2.168665 | 1.798605 | 1.798605 | 0.00000 | 0.000000 | 0.000000 | 0.370528 | 0.608418 | 2.331004 | 0.932402 | 19.719451 | 0.000000 |
| 4 | 10.860082 | 0.694965 | 3.604094 | 0.447332 | 0.483726 | 0.036394 | 0.989498 | 0.542166 | 0.665849 | 0.603791 | 0.00000 | 0.030401 | 0.000000 | 2.259878 | 2.218822 | 2.331004 | 4.195807 | 22.306992 | 0.057947 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 999995 | 10.860082 | 0.000000 | 0.671967 | 0.000000 | 0.000000 | 0.000000 | 3.352469 | 3.352469 | 0.099471 | 0.099471 | 0.00000 | 0.487509 | 0.487509 | 0.369804 | 0.369804 | 1.864803 | 2.536770 | 17.204439 | 0.000000 |
| 999996 | 8.480702 | 0.694965 | 2.138030 | 0.229919 | 2.151195 | 1.921276 | 1.725970 | 1.496051 | 1.798605 | 1.798605 | 0.00000 | 2.437544 | 2.437544 | 1.852641 | 2.548761 | 0.000000 | 0.466201 | 13.985866 | 0.000000 |
| 999997 | 8.778125 | 0.521223 | 3.237578 | 0.316884 | 1.469650 | 1.152766 | 1.725970 | 1.409086 | 0.883262 | 0.566378 | 0.00000 | 1.478769 | 1.445357 | 1.524338 | 1.479215 | 0.045123 | 0.000000 | 18.011220 | 0.063688 |
| 999998 | 9.967815 | 0.521223 | 1.771514 | 0.000000 | 1.636492 | 1.636492 | 1.084333 | 1.084333 | 1.232227 | 1.232227 | 0.00000 | 1.462527 | 1.462527 | 1.479215 | 1.479215 | 0.932402 | 2.703916 | 16.168368 | 0.000000 |
| 999999 | 10.562660 | 1.216188 | 0.671967 | 0.273401 | 2.194677 | 1.921276 | 3.352469 | 3.079067 | 0.556591 | 0.283189 | 0.00000 | 2.953313 | 2.925053 | 0.000000 | 0.000000 | 1.864803 | 1.864803 | 20.419944 | 0.053867 |
1000000 rows × 19 columns
[20]:
df_geral = df.copy()
df_restrito = df.copy()
df_restrito = df_restrito.loc[(df['Biomassa']>=0.05)&(df['Produção de NADH'] >=24),]
df_geral.drop(index=df_restrito.index,inplace=True)
df_geral['Label'] = ['Geral' for i in range(len(df_geral))]
[4]:
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | Label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10.562660 | 1.216188 | 1.404998 | 0.483726 | 3.557768 | 3.074042 | 1.025892 | 0.542166 | 1.232227 | 1.232227 | 0.00000 | 1.478769 | 1.478769 | 0.741057 | 1.224783 | 0.000000 | 0.000000 | 16.679557 | 0.000000 | Geral |
| 1 | 9.670392 | 0.173741 | 2.871062 | 0.360367 | 0.867981 | 0.507615 | 0.360367 | 0.000000 | 0.360367 | 0.000000 | 0.09218 | 0.492923 | 0.582421 | 1.852641 | 1.849019 | 2.331004 | 4.195807 | 17.698335 | 0.005113 | Geral |
| 2 | 9.967815 | 1.389929 | 3.604094 | 1.183804 | 3.489335 | 2.305531 | 1.725970 | 0.542166 | 1.750182 | 0.566378 | 0.00000 | 2.454197 | 2.437544 | 1.131901 | 1.109411 | 0.932402 | 0.932402 | 19.835517 | 0.031742 | Geral |
| 3 | 11.157505 | 0.173741 | 2.504546 | 1.636492 | 3.173513 | 1.537021 | 3.805157 | 2.168665 | 1.798605 | 1.798605 | 0.00000 | 0.000000 | 0.000000 | 0.370528 | 0.608418 | 2.331004 | 0.932402 | 19.719451 | 0.000000 | Geral |
| 4 | 10.860082 | 0.694965 | 3.604094 | 0.447332 | 0.483726 | 0.036394 | 0.989498 | 0.542166 | 0.665849 | 0.603791 | 0.00000 | 0.030401 | 0.000000 | 2.259878 | 2.218822 | 2.331004 | 4.195807 | 22.306992 | 0.057947 | Geral |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 999995 | 10.860082 | 0.000000 | 0.671967 | 0.000000 | 0.000000 | 0.000000 | 3.352469 | 3.352469 | 0.099471 | 0.099471 | 0.00000 | 0.487509 | 0.487509 | 0.369804 | 0.369804 | 1.864803 | 2.536770 | 17.204439 | 0.000000 | Geral |
| 999996 | 8.480702 | 0.694965 | 2.138030 | 0.229919 | 2.151195 | 1.921276 | 1.725970 | 1.496051 | 1.798605 | 1.798605 | 0.00000 | 2.437544 | 2.437544 | 1.852641 | 2.548761 | 0.000000 | 0.466201 | 13.985866 | 0.000000 | Geral |
| 999997 | 8.778125 | 0.521223 | 3.237578 | 0.316884 | 1.469650 | 1.152766 | 1.725970 | 1.409086 | 0.883262 | 0.566378 | 0.00000 | 1.478769 | 1.445357 | 1.524338 | 1.479215 | 0.045123 | 0.000000 | 18.011220 | 0.063688 | Geral |
| 999998 | 9.967815 | 0.521223 | 1.771514 | 0.000000 | 1.636492 | 1.636492 | 1.084333 | 1.084333 | 1.232227 | 1.232227 | 0.00000 | 1.462527 | 1.462527 | 1.479215 | 1.479215 | 0.932402 | 2.703916 | 16.168368 | 0.000000 | Geral |
| 999999 | 10.562660 | 1.216188 | 0.671967 | 0.273401 | 2.194677 | 1.921276 | 3.352469 | 3.079067 | 0.556591 | 0.283189 | 0.00000 | 2.953313 | 2.925053 | 0.000000 | 0.000000 | 1.864803 | 1.864803 | 20.419944 | 0.053867 | Geral |
999751 rows × 20 columns
[5]:
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 682 | 10.265237 | 1.563670 | 2.871062 | 0.142954 | 0.527209 | 0.384255 | 1.183804 | 1.040850 | 0.426143 | 0.283189 | 0.000000 | 0.519631 | 0.487509 | 0.370528 | 0.369804 | 4.195807 | 4.195807 | 24.417125 | 0.061228 |
| 1251 | 10.860082 | 1.389929 | 3.604094 | 0.360367 | 0.360367 | 0.000000 | 2.810302 | 2.449936 | 0.643556 | 0.283189 | 0.000000 | 1.971692 | 1.945435 | 0.035460 | 0.000000 | 4.195807 | 4.195807 | 24.657655 | 0.050049 |
| 11359 | 11.157505 | 1.042447 | 3.237578 | 0.490815 | 0.490815 | 0.000000 | 2.117313 | 1.626499 | 1.340382 | 0.849567 | 0.061453 | 0.492923 | 0.526793 | 0.407055 | 0.369804 | 4.195807 | 4.195807 | 24.592242 | 0.052577 |
| 11831 | 10.562660 | 1.389929 | 2.871062 | 0.309687 | 0.483726 | 0.174040 | 0.851853 | 0.542166 | 0.592876 | 0.283189 | 0.245814 | 0.000000 | 0.214524 | 1.151667 | 1.109411 | 3.729606 | 3.729606 | 24.214517 | 0.059641 |
| 13024 | 10.562660 | 1.563670 | 3.604094 | 0.099471 | 0.099471 | 0.000000 | 1.183804 | 1.084333 | 0.949038 | 0.849567 | 0.000000 | 0.518868 | 0.487509 | 0.412153 | 0.369804 | 3.263405 | 3.263405 | 24.057084 | 0.059772 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 967341 | 11.157505 | 1.216188 | 3.604094 | 0.447332 | 0.483726 | 0.036394 | 1.531665 | 1.084333 | 0.730521 | 0.283189 | 0.153634 | 0.492923 | 0.614988 | 0.412436 | 0.369804 | 2.797205 | 3.263405 | 24.006515 | 0.060172 |
| 973918 | 10.562660 | 1.042447 | 3.237578 | 0.192313 | 0.960823 | 0.768510 | 0.192313 | 0.000000 | 0.192313 | 0.000000 | 0.122907 | 0.492923 | 0.581830 | 0.785524 | 0.739607 | 3.729606 | 4.195807 | 24.080630 | 0.064808 |
| 977070 | 10.265237 | 1.563670 | 3.237578 | 0.099471 | 1.252237 | 1.152766 | 0.099471 | 0.000000 | 0.949038 | 0.849567 | 0.000000 | 0.518514 | 0.487509 | 0.000000 | 0.000000 | 4.195807 | 4.195807 | 24.469771 | 0.059098 |
| 978778 | 11.157505 | 1.389929 | 3.604094 | 0.338586 | 0.867981 | 0.529395 | 1.965085 | 1.626499 | 0.338586 | 0.000000 | 0.276540 | 0.492923 | 0.739069 | 0.041047 | 0.000000 | 2.838252 | 2.797205 | 24.245067 | 0.057935 |
| 988765 | 10.265237 | 1.563670 | 2.504546 | 0.316884 | 0.701139 | 0.384255 | 0.316884 | 0.000000 | 0.600073 | 0.283189 | 0.245814 | 0.000000 | 0.213570 | 0.783151 | 0.739607 | 3.773150 | 3.729606 | 24.031348 | 0.061459 |
249 rows × 19 columns
[21]:
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | Label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10.562660 | 1.216188 | 1.404998 | 0.483726 | 3.557768 | 3.074042 | 1.025892 | 0.542166 | 1.232227 | 1.232227 | 0.000000 | 1.478769 | 1.478769 | 0.741057 | 1.224783 | 0.000000 | 0.000000 | 16.6796 | 0.000000 | Geral |
| 1 | 9.670392 | 0.173741 | 2.871062 | 0.360367 | 0.867981 | 0.507615 | 0.360367 | 0.000000 | 0.360367 | 0.000000 | 0.092180 | 0.492923 | 0.582421 | 1.852641 | 1.849019 | 2.331004 | 4.195807 | 17.6983 | 0.005113 | Geral |
| 2 | 9.967815 | 1.389929 | 3.604094 | 1.183804 | 3.489335 | 2.305531 | 1.725970 | 0.542166 | 1.750182 | 0.566378 | 0.000000 | 2.454197 | 2.437544 | 1.131901 | 1.109411 | 0.932402 | 0.932402 | 19.8355 | 0.031742 | Geral |
| 4 | 10.860082 | 0.694965 | 3.604094 | 0.447332 | 0.483726 | 0.036394 | 0.989498 | 0.542166 | 0.665849 | 0.603791 | 0.000000 | 0.030401 | 0.000000 | 2.259878 | 2.218822 | 2.331004 | 4.195807 | 22.3070 | 0.057947 | Geral |
| 5 | 8.480702 | 1.389929 | 2.504546 | 0.316884 | 0.316884 | 0.000000 | 0.316884 | 0.000000 | 1.232227 | 1.132756 | 0.030727 | 0.492923 | 0.492814 | 1.520858 | 1.479215 | 1.398602 | 1.398602 | 19.3749 | 0.058776 | Geral |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 999232 | 11.157505 | 0.347482 | 2.138030 | 0.447332 | 2.020747 | 1.573415 | 2.073831 | 1.626499 | 2.081794 | 1.982324 | 0.000000 | 3.428322 | 3.412562 | 1.500499 | 1.479215 | 2.700149 | 2.331004 | 20.4540 | 0.030040 | Geral |
| 999575 | 10.860082 | 0.347482 | 2.871062 | 0.099471 | 0.867981 | 0.768510 | 3.352469 | 3.252998 | 2.648173 | 2.548702 | 0.030727 | 1.478769 | 1.488136 | 0.768453 | 0.739607 | 3.758452 | 3.729606 | 21.8867 | 0.040713 | Geral |
| 999724 | 10.860082 | 1.216188 | 2.138030 | 1.636492 | 2.789257 | 1.152766 | 4.889489 | 3.252998 | 1.232227 | 0.566378 | 0.000000 | 1.467821 | 1.462527 | 2.223170 | 2.218822 | 0.974990 | 0.000000 | 19.2799 | 0.010091 | Geral |
| 999797 | 9.372970 | 0.347482 | 3.237578 | 0.483726 | 2.789257 | 2.305531 | 1.568059 | 1.084333 | 1.232227 | 1.192257 | 0.000000 | 1.482107 | 1.462527 | 2.984873 | 2.958430 | 3.729606 | 3.263405 | 20.9188 | 0.037322 | Geral |
| 999894 | 8.778125 | 0.521223 | 2.504546 | 1.252237 | 2.789257 | 1.537021 | 2.336569 | 1.084333 | 0.949038 | 0.936683 | 0.000000 | 3.418615 | 3.412562 | 1.857193 | 1.849019 | 0.466201 | 0.466201 | 16.0096 | 0.011537 | Geral |
37142 rows × 20 columns
[22]:
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | Label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 682 | 10.265237 | 1.563670 | 2.871062 | 0.142954 | 0.527209 | 0.384255 | 1.183804 | 1.040850 | 0.426143 | 0.283189 | 0.000000 | 0.519631 | 0.487509 | 0.370528 | 0.369804 | 4.195807 | 4.195807 | 24.42 | 0.061228 | Melhores |
| 1251 | 10.860082 | 1.389929 | 3.604094 | 0.360367 | 0.360367 | 0.000000 | 2.810302 | 2.449936 | 0.643556 | 0.283189 | 0.000000 | 1.971692 | 1.945435 | 0.035460 | 0.000000 | 4.195807 | 4.195807 | 24.66 | 0.050049 | Melhores |
| 11359 | 11.157505 | 1.042447 | 3.237578 | 0.490815 | 0.490815 | 0.000000 | 2.117313 | 1.626499 | 1.340382 | 0.849567 | 0.061453 | 0.492923 | 0.526793 | 0.407055 | 0.369804 | 4.195807 | 4.195807 | 24.59 | 0.052577 | Melhores |
| 11831 | 10.562660 | 1.389929 | 2.871062 | 0.309687 | 0.483726 | 0.174040 | 0.851853 | 0.542166 | 0.592876 | 0.283189 | 0.245814 | 0.000000 | 0.214524 | 1.151667 | 1.109411 | 3.729606 | 3.729606 | 24.21 | 0.059641 | Melhores |
| 13024 | 10.562660 | 1.563670 | 3.604094 | 0.099471 | 0.099471 | 0.000000 | 1.183804 | 1.084333 | 0.949038 | 0.849567 | 0.000000 | 0.518868 | 0.487509 | 0.412153 | 0.369804 | 3.263405 | 3.263405 | 24.06 | 0.059772 | Melhores |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 875919 | 10.265237 | 0.868706 | 3.237578 | 0.099471 | 0.867981 | 0.768510 | 0.099471 | 0.000000 | 0.099471 | 0.000000 | 0.000000 | 0.037844 | 0.000000 | 0.000000 | 0.000000 | 4.195807 | 4.195807 | 24.38 | 0.072134 | Melhores |
| 918573 | 10.860082 | 1.389929 | 3.237578 | 0.447332 | 0.447332 | 0.000000 | 3.352469 | 2.905137 | 0.730521 | 0.283189 | 0.000000 | 0.027327 | 0.000000 | 0.741057 | 0.749979 | 4.186884 | 4.195807 | 24.61 | 0.052087 | Melhores |
| 945138 | 11.157505 | 1.042447 | 3.237578 | 0.099471 | 0.099471 | 0.000000 | 0.099471 | 0.000000 | 0.382660 | 0.283189 | 0.000000 | 1.980634 | 1.950035 | 0.370528 | 0.370528 | 4.195807 | 4.195807 | 24.62 | 0.058324 | Melhores |
| 958074 | 11.157505 | 1.042447 | 3.604094 | 0.447332 | 1.600098 | 1.152766 | 0.641637 | 0.194305 | 0.730521 | 0.283189 | 0.000000 | 0.031451 | 0.000000 | 0.412277 | 0.369804 | 3.305879 | 3.263405 | 24.25 | 0.059948 | Melhores |
| 958825 | 10.562660 | 1.042447 | 3.604094 | 0.192834 | 0.192834 | 0.000000 | 0.735000 | 0.542166 | 0.476023 | 0.283189 | 0.122907 | 0.492923 | 0.581575 | 0.046261 | 0.000000 | 4.195807 | 4.195807 | 24.84 | 0.065294 | Melhores |
78 rows × 20 columns
<ggplot: (640 x 480)>
[24]:
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | Label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10.562660 | 1.216188 | 1.404998 | 0.483726 | 3.557768 | 3.074042 | 1.025892 | 0.542166 | 1.232227 | 1.232227 | 0.000000 | 1.478769 | 1.478769 | 0.741057 | 1.224783 | 0.000000 | 0.000000 | 16.6796 | 0.000000 | Geral |
| 1 | 9.670392 | 0.173741 | 2.871062 | 0.360367 | 0.867981 | 0.507615 | 0.360367 | 0.000000 | 0.360367 | 0.000000 | 0.092180 | 0.492923 | 0.582421 | 1.852641 | 1.849019 | 2.331004 | 4.195807 | 17.6983 | 0.005113 | Geral |
| 2 | 9.967815 | 1.389929 | 3.604094 | 1.183804 | 3.489335 | 2.305531 | 1.725970 | 0.542166 | 1.750182 | 0.566378 | 0.000000 | 2.454197 | 2.437544 | 1.131901 | 1.109411 | 0.932402 | 0.932402 | 19.8355 | 0.031742 | Geral |
| 4 | 10.860082 | 0.694965 | 3.604094 | 0.447332 | 0.483726 | 0.036394 | 0.989498 | 0.542166 | 0.665849 | 0.603791 | 0.000000 | 0.030401 | 0.000000 | 2.259878 | 2.218822 | 2.331004 | 4.195807 | 22.3070 | 0.057947 | Geral |
| 5 | 8.480702 | 1.389929 | 2.504546 | 0.316884 | 0.316884 | 0.000000 | 0.316884 | 0.000000 | 1.232227 | 1.132756 | 0.030727 | 0.492923 | 0.492814 | 1.520858 | 1.479215 | 1.398602 | 1.398602 | 19.3749 | 0.058776 | Geral |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 875919 | 10.265237 | 0.868706 | 3.237578 | 0.099471 | 0.867981 | 0.768510 | 0.099471 | 0.000000 | 0.099471 | 0.000000 | 0.000000 | 0.037844 | 0.000000 | 0.000000 | 0.000000 | 4.195807 | 4.195807 | 24.3800 | 0.072134 | Melhores |
| 918573 | 10.860082 | 1.389929 | 3.237578 | 0.447332 | 0.447332 | 0.000000 | 3.352469 | 2.905137 | 0.730521 | 0.283189 | 0.000000 | 0.027327 | 0.000000 | 0.741057 | 0.749979 | 4.186884 | 4.195807 | 24.6100 | 0.052087 | Melhores |
| 945138 | 11.157505 | 1.042447 | 3.237578 | 0.099471 | 0.099471 | 0.000000 | 0.099471 | 0.000000 | 0.382660 | 0.283189 | 0.000000 | 1.980634 | 1.950035 | 0.370528 | 0.370528 | 4.195807 | 4.195807 | 24.6200 | 0.058324 | Melhores |
| 958074 | 11.157505 | 1.042447 | 3.604094 | 0.447332 | 1.600098 | 1.152766 | 0.641637 | 0.194305 | 0.730521 | 0.283189 | 0.000000 | 0.031451 | 0.000000 | 0.412277 | 0.369804 | 3.305879 | 3.263405 | 24.2500 | 0.059948 | Melhores |
| 958825 | 10.562660 | 1.042447 | 3.604094 | 0.192834 | 0.192834 | 0.000000 | 0.735000 | 0.542166 | 0.476023 | 0.283189 | 0.122907 | 0.492923 | 0.581575 | 0.046261 | 0.000000 | 4.195807 | 4.195807 | 24.8400 | 0.065294 | Melhores |
37220 rows × 20 columns
Kernel status: Idle
/home/cristian/.conda/envs/escher/bin/python
Set parameter Username Set parameter LicenseID to value 2644080 Academic license - for non-commercial use only - expires 2026-03-29
[6]:
#model.reactions.get_by_id('PDH').bounds = (0.19,0.19)
model.reactions.get_by_id('FUM').bounds = (0.27,0.27)
model.reactions.get_by_id('PPC').bounds = (22,22)
[7]:
solution = model.optimize()
escher_builder = escher.Builder(
map_name='iJO1366.Central metabolism',
reaction_scale=[{'type': 'min', 'color': '#0000ff', 'size': 10},
{'type': 'mean', 'color': '#551a8b', 'size': 20},
{'type': 'max', 'color': '#ff0000', 'size': 40}],
hide_secondary_metabolites = True,
reaction_data = solution.fluxes,
)
display(escher_builder)
[16]:
solution
[16]:
Optimal solution with objective value 0.190
| fluxes | reduced_costs | |
|---|---|---|
| EX_cm_e | 0.000000 | 0.000000e+00 |
| EX_cmp_e | 0.000000 | -5.417533e-01 |
| EX_co2_e | 2.574095 | 0.000000e+00 |
| EX_cobalt2_e | -0.000005 | 0.000000e+00 |
| DM_4crsol_c | 0.000042 | 0.000000e+00 |
| ... | ... | ... |
| RNDR4 | 0.000000 | -3.833160e-03 |
| RNDR4b | 0.000000 | -3.833160e-03 |
| RNTR1c2 | 0.004964 | 0.000000e+00 |
| RNTR2c2 | 0.005126 | 0.000000e+00 |
| RNTR3c2 | 0.005126 | 2.775558e-17 |
2583 rows × 2 columns
[ ]:
## PDH
PDH¶
Read LP format model from file /tmp/tmptzywuw6q.lp Reading time = 0.01 seconds : 1805 rows, 5166 columns, 20366 nonzeros
Error displaying widget: model not found
- Melhores Fluxos Interativo.ipynb
- IC_pt1.ipynb
- IC_pt2.ipynb
- Clusters.ipynb
- Escher.ipynb
- Grafico.ipynb
- Modificacao_iCH360.ipynb
Common Tools
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Advanced Tools
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Variables
Callstack
Breakpoints
Source
9
1